TECNALIA Publications Repository :: Browsing by Author "Landa-Torres, Itziar"

Browsing by Author "Landa-Torres, Itziar"

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Adaptive Dendritic Cell-Deep Learning Approach for Industrial Prognosis under Changing Conditions
(2021-11) Diez-Olivan, Alberto; Ortego, Patxi; Ser, Javier Del; Landa-Torres, Itziar; Galar, Diego; Camacho, David; Sierra, Basilio; Tecnalia Research & Innovation; IA
Industrial prognosis refers to the prediction of failures of an industrial asset based on data collected by Internet of Things sensors. Prognostic models can experience the undesired effects of concept drift, namely, the presence of nonstationary phenomena that affects the data collected over time. Consequently, fault patterns learned from data become obsolete. To overcome this issue, contextual and operational changes must be detected and managed, triggering rapid model adaptation mechanisms. This article presents an adaptive learning approach based on a dendritic cell algorithm for drift detection and a deep neural network model that dynamically adapts to new operational conditions. A kernel density estimator with drift-based bandwidth is used to generate synthetic data for a faster adaptation, focusing on fine-tuning the lowest neural layers. Experimental results over a real-world industrial problem shed light on the outperforming behavior of the proposed approach when compared to other drift detectors and classification models.
Analysis and Application of Normalization Methods with Supervised Feature Weighting to Improve K-means Accuracy
(Springer Verlag, 2020) Niño-Adan, Iratxe; Landa-Torres, Itziar; Portillo, Eva; Manjarres, Diana; Martínez Álvarez, Francisco; Troncoso Lora, Alicia; Quintián, Héctor; Sáez Muñoz, José António; Corchado, Emilio; Tecnalia Research & Innovation; IA
Normalization methods are widely employed for transforming the variables or features of a given dataset. In this paper three classical feature normalization methods, Standardization (St), Min-Max (MM) and Median Absolute Deviation (MAD), are studied in different synthetic datasets from UCI repository. An exhaustive analysis of the transformed features’ ranges and their influence on the Euclidean distance is performed, concluding that knowledge about the group structure gathered by each feature is needed to select the best normalization method for a given dataset. In order to effectively collect the features’ importance and adjust their contribution, this paper proposes a two-stage methodology for normalization and supervised feature weighting based on a Pearson correlation coefficient and on a Random Forest Feature Importance estimation method. Simulations on five different datasets reveal that our two-stage proposed methodology, in terms of accuracy, outperforms or at least maintains the K-means performance obtained if only normalization is applied.
Evaluating the internationalization success of companies through a hybrid grouping harmony search-extreme learning machine approach
(2012) Landa-Torres, Itziar; Ortiz-García, Emilio G.; Salcedo-Sanz, Sancho; Segovia-Vargas, María J.; Gil-López, Sergio; Miranda, Marta; Leiva-Murillo, Jose M.; Del Ser, Javier; Tecnalia Research & Innovation; IA
The internationalization of a company is widely understood as the corporative strategy for growing through external markets. It usually embodies a hard process, which affects diverse activities of the value chain and impacts on the organizational structure of the company. There is not a general model for a successful international company, so the success of an internationalization procedure must be estimated based on different variables addressing the status, strategy and market characteristics of the company at hand. This paper presents a novel hybrid soft-computing approach for evaluating the internationalization success of a company based on existing past data. Specifically, we propose a hybrid algorithm composed by a grouping-based harmony search (HS) approach and an extreme learning machine (ELM) ensemble. The proposed hybrid scheme further incorporates a feature selection method, which is obtained by means of a given group in the HS encoding format, whereas the ELM ensemble renders the final accuracy metric of the model. Practical results for the proposed hybrid technique are obtained in a real application based on the exporting success of Spanish manufacturing companies, which are shown to be satisfactory in comparison with alternative state-of-the-art techniques.
Feature weighting methods: A review
(2021-12-01) Niño-Adan, Iratxe; Manjarres, Diana; Landa-Torres, Itziar; Portillo, Eva; Tecnalia Research & Innovation; IA
In the last decades, a wide portfolio of Feature Weighting (FW) methods have been proposed in the literature. Their main potential is the capability to transform the features in order to contribute to the Machine Learning (ML) algorithm metric proportionally to their estimated relevance for inferring the output pattern. Nevertheless, the extensive number of FW related works makes difficult to do a scientific study in this field of knowledge. Therefore, in this paper a global taxonomy for FW methods is proposed by focusing on: (1) the learning approach (supervised or unsupervised), (2) the methodology used to calculate the weights (global or local), and (3) the feedback obtained from the ML algorithm when estimating the weights (filter or wrapper). Among the different taxonomy levels, an extensive review of the state-of-the-art is presented, followed by some considerations and guide points for the FW strategies selection regarding significant aspects of real-world data analysis problems. Finally, a summary of conclusions and challenges in the FW field is briefly outlined.
A grouping harmony search approach for the Citywide WiFi deployment problem
(2011) Landa-Torres, Itziar; Gil-Lopez, Sergio; Del Ser, Javier; Salcedo-Sanz, Sancho; Manjarres, Diana; Portilla-Figueras, J. A.; Tecnalia Research & Innovation; IA
This paper presents a novel Grouping Harmony Search (GHS) algorithm for the Citywide Ubiquitous WiFi Network Design problem (WIFIDP). The WIFIDP is a NP-hard problem where private customers owning wireless access points connected to Internet share bandwidth with third parties. Aspects such as allocated budget and router capacities (coverage radius, capacity, price, etc) are taken into account in order to obtain the optimal network deployment (in terms of cost-effectiveness) when applying the GHS algorithm. The approach to tackle the aforementioned WIFIDP problem consists of a hybrid Grouping Harmony Search (GHS) algorithm with a local search method and a technique for repairing unfeasible solutions. Furthermore, the presented GHS algorithm is differential, since each proposed harmony is produced (improvised) based on the same harmony in the previous iteration. This differential scheme employs the grouping concept based on the connectivity between nomadic users and routers, which increases significantly its searching capability. Preliminary Monte Carlo simulations show that this proposed technique statistically outperforms genetically-inspired algorithms previously presented for the WIFIDP, with an emphasis in scenarios with stringent capacity and budget constraints. This first approach paves the way for future research aimed at applying the proposed algorithm to real scenarios.
A hyper-heuristic inspired approach for automatic failure prediction in the context of industry 4.0
(2022-09) Navajas-Guerrero, Adriana; Manjarres, Diana; Portillo, Eva; Landa-Torres, Itziar; IA
In the era of technological advances and Industry 4.0, massive data collection and analysis is a common approach followed by many industries and companies worldwide. One of the most important uses of data mining and Machine Learning techniques is to predict possible breaks or failures in industrial processes or machinery. This research designs and develops a hyper-heuristic inspired methodology to autonomously identify significant parameters of the time series that characterize the behaviour of relevant process variables enabling the prediction of failures. The proposed hyper-heuristic inspired approach is based on the combination of an optimization process performed by a meta-heuristic algorithm (Harmony Search) and feature based statistical methods for anomaly detection. It demonstrates its adaptability to different failure cases without expert domain knowledge and the capability of autonomously identifying most relevant parameters of the time series to detect the abnormal behaviour prior to the final failure. The proposed solution is validated against a real database of a cold stamping process yielding satisfactory results respect to a novel AUC_ROC based metric, named AUC_MOD, and other conventional metrics, i.e., Specificity, Sensitivity and False Positive Rate.
Influence of statistical feature normalisation methods on K-Nearest Neighbours and K-Means in the context of industry 4.0
(2022-05) Niño-Adan, Iratxe; Landa-Torres, Itziar; Portillo, Eva; Manjarres, Diana; Tecnalia Research & Innovation; IA
Normalisation is a preprocessing technique widely employed in Machine Learning (ML)-based solutions for industry to equalise the features’ contribution. However, few researchers have analysed the normalisation effect and its implications on the ML algorithm performance, especially on Euclidean distance-based algorithms, such as the well-known K-Nearest Neighbours and K-means. In this sense, this paper formally analyses the effect of normalisation yielding results significantly far from the state-of-the-art traditional claims. In particular, this paper shows that normalisation does not equalise the contribution of the features, with the consequent impact on the performance of the learning process for a particular problem. More concretely, this demonstration is made on K-Nearest Neighbours and K-means Euclidean distance-based ML algorithms. This paper concludes that normalisation can be viewed as an unsupervised Feature Weighting method. In this context, a new metric (Normalisation weight) for measuring the impact of normalisation on the features is presented. Likewise, an analysis of the normalisation effect on the Euclidean distance is conducted and a new metric referred to as Proportional influence that measures the features influence on the Euclidean distance is proposed. Both metrics enable the automatic selection of the most appropriate normalisation method for a particular engineering problem, which can significantly improve both the computational cost and classification performance of K-Nearest Neighbours and K-means algorithms. The analytical conclusions are validated on well-known datasets from the UCI repository and a real-life application from the refinery industry.
An intelligent decision support system for assessing the default risk in small and medium-sized enterprises
(Springer Verlag, 2017) Manjarres, Diana; Landa-Torres, Itziar; Andonegui, Imanol; Zurada, Jacek M.; Zadeh, Lotfi A.; Tadeusiewicz, Ryszard; Rutkowski, Leszek; Korytkowski, Marcin; Scherer, Rafal; IA; Tecnalia Research & Innovation
In the last years, default prediction systems have become an important tool for a wide variety of financial institutions, such as banking systems or credit business, for which being able of detecting credit and default risks, translates to a better financial status. Nevertheless, small and medium-sized enterprises did not focus its attention on customer default prediction but in maximizing the sales rate. Consequently, many companies could not cope with the customers’ debt and ended up closing the business. In order to overcome this issue, this paper presents a novel decision support system for default prediction specially tailored for small and medium-sized enterprises that retrieves the information related to the customers in an Enterprise Resource Planning (ERP) system and obtain the default risk probability of a new order or client. The resulting approach has been tested in a Graphic Arts printing company of The Basque Country allowing taking prioritized and preventive actions with regard to the default risk probability and the customer’s characteristics. Simulation results verify that the proposed scheme achieves a better performance than a naïve Random Forest (RF) classification technique in real scenarios with unbalanced datasets.
A local search method for graph clustering heuristics based on partitional Distribution learning
(Institute of Electrical and Electronics Engineers Inc., 2017-07-05) Manjarres, Diana; Landa-Torres, Itziar; Del Ser, Javier; IA; Tecnalia Research & Innovation
The community structure of complex networks reveals hidden relationships in the organization of their constituent nodes. Indeed, many practical problems stemming from different fields of knowledge such as Biology, Sociology, Chemistry and Computer Science can be modeled as a graph. Therefore, graph analysis and community detection have become a key component for understanding the inherent relational characteristics underlying different systems and processes. In this regard, distinct unsupervised quality metrics such as conductance, coverage and modularity, have upsurged in order to evaluate the clustering arrangements based on structural and topological characteristics of the cluster space. In this regard graph clustering can be formulated as an optimization problem based on the maximization of one of such metrics, for which a number of nature-inspired heuristic solvers has been proposed in the literature. This paper elaborates on a novel local search method that allows boosting the convergence of such heuristics by estimating and sampling the cluster arrangement distribution from the set of intermediate produced solutions of the algorithm at hand. Simulation results reveal a generalized better performance compared towards other community detection algorithms in synthetic and real datasets.
Machine learning based adaptive soft sensor for flash point inference in a refinery realtime process
(2022-03) Mendia, Izaskun; Gil-López, Sergio; Landa-Torres, Itziar; Orbe, Lucía; Maqueda, Erik; Tecnalia Research & Innovation; IA; DIGITAL ENERGY
In industrial control processes, certain characteristics are sometimes difficult to measure by a physical sensor due to technical and/or economic limitations. This fact is especially true in the petrochemical industry. Some of those quantities are especially crucial for operators and process safety. This is the case for the automotive diesel Flash Point Temperature (FT). Traditional methods for FT estimation are based on the study of the empirical inference between flammability properties and the denoted target magnitude. The necessary measures are taken indirectly by samples from the process and analyzing them in the laboratory, this process implies time (can take hours from collection to flash temperature measurement) and thus make it very difficult for real-time monitorization, which in fact results in security and economical losses. This study defines a procedure based on Machine Learning modules that demonstrate the power of real-time monitorization over real data from an important international refinery. As input, easily measured values provided in real-time, such as temperature, pressure, and hydraulic flow are used and a benchmark of different regressive algorithms for FT estimation is presented. The study highlights the importance of sequencing preprocessing techniques for the correct inference of values. The implementation of adaptive learning strategies achieves considerable economic benefits in the productization of this soft sensor. The validity of the method is tested in the reality of a refinery. In addition, real-world industrial data sets tend to be unstable and volatile, and the data is often affected by noise, outliers, irrelevant or unnecessary features, and missing data. This contribution demonstrates with the inclusion of a new concept, called an adaptive soft sensor, the importance of the dynamic adaptation of the conformed schemes based on Machine Learning through their combination with feature selection, dimensional reduction, and signal processing techniques. The economic benefits of applying this soft sensor in the refinery's production plant and presented as potential semi-annual savings.
A Multi-objective Harmony Search Algorithm for Optimal Energy and Environmental Refurbishment at District Level Scale
(Springer Singapore, 2017) Manjarres, Diana; Mabe, Lara; Oregi, Xabat; Landa-Torres, Itziar; Arrizabalaga, Eneko; Del Ser, Javier; Tecnalia Research & Innovation; IA; PLANIFICACIÓN ENERGÉTICA
Nowadays municipalities are facing an increasing commitment regarding the energy and environmental performance of cities and districts. The multiple factors that characterize a district scenario, such as: refurbishment strategies’ selection, combination of passive, active and control measures, the surface to be refurbished and the generation systems to be substituted will highly influence the final impacts of the refurbishment solution. In order to answer this increasing demand and consider all above-mentioned district factors, municipalities need optimisation methods supporting the decision making process at district level scale when defining cost-effective refurbishment scenarios. Furthermore, the optimisation process should enable the evaluation of feasible solutions at district scale taking into account that each district and building has specific boundaries and barriers. Considering these needs, this paper presents a multi-objective approach allowing a simultaneous environmental and economic assessment of refurbishment scenarios at district scale. With the aim at demonstrating the effectiveness of the proposed approach, a real scenario of Gros district in the city of Donostia-San Sebastian (North of Spain) is presented. After analysing the baseline scenario in terms of energy performance, environmental and economic impacts, the multi-objective Harmony Search algorithm has been employed to assess the goal of reducing the environmental impacts in terms of Global Warming Potential (GWP) and minimizing the investment cost obtaining the best ranking of economic and environmental refurbishment scenarios for the Gros district.
Multi-objective heuristics applied to robot task planning for inspection plants
(Institute of Electrical and Electronics Engineers Inc., 2017-07-05) Landa-Torres, Itziar; Lobo, Jesus L.; Murua, Idoia; Manjarres, Diana; Del Ser, Javier; Tecnalia Research & Innovation; IA; HPA
Robotics are generally subject to stringent operational conditions that impose a high degree of criticality on the allocation of resources and the schedule of operations in mission planning. In this regard the so-called cost of a mission must be considered as an additional criterion when designing optimal task schedules within the mission at hand. Such a cost can be conceived as the impact of the mission on the robotic resources themselves, which range from the consumption of battery to other negative effects such as mechanic erosion. This manuscript focuses on this issue by presenting experimental results obtained over realistic scenarios of two heuristic solvers (MOHS and NSGA-II) aimed at efficiently scheduling tasks in robotic swarms that collaborate together to accomplish a mission. The heuristic techniques resort to a Random-Keys encoding strategy to represent the allocation of robots to tasks whereas the relative execution order of such tasks within the schedule of certain robots is computed based on the Traveling Salesman Problem (TSP). Experimental results in three different deployment scenarios reveal the goodness of the proposed technique based on the Multi-objective Harmony Search algorithm (MOHS) in terms of Hypervolume (HV) and Coverage Rate (CR) performance indicators.
Normalization Influence on ANN-Based Models Performance: A New Proposal for Features’ Contribution Analysis: A New Proposal for Features' Contribution Analysis
(2021) Nino-Adan, Iratxe; Portillo, Eva; Landa-Torres, Itziar; Manjarres, Diana; Tecnalia Research & Innovation; IA
Artificial Neural Networks (ANNs) are weighted directed graphs of interconnected neurons widely employed to model complex problems. However, the selection of the optimal ANN architecture and its training parameters is not enough to obtain reliable models. The data preprocessing stage is fundamental to improve the model’s performance. Specifically, Feature Normalisation (FN) is commonly utilised to remove the features’ magnitude aiming at equalising the features’ contribution to the model training. Nevertheless, this work demonstrates that the FN method selection affects the model performance. Also, it is well-known that ANNs are commonly considered a “black box” due to their lack of interpretability. In this sense, several works aim to analyse the features’ contribution to the network for estimating the output. However, these methods, specifically those based on network’s weights, like Garson’s or Yoon’s methods, do not consider preprocessing factors, such as dispersion factors , previously employed to transform the input data. This work proposes a new features’ relevance analysis method that includes the dispersion factors into the weight matrix analysis methods to infer each feature’s actual contribution to the network output more precisely. Besides, in this work, the Proportional Dispersion Weights (PWD) are proposed as explanatory factors of similarity between models’ performance results. The conclusions from this work improve the understanding of the features’ contribution to the model that enhances the feature selection strategy, which is fundamental for reliably modelling a given problem.
A novel grouping harmony search algorithm for clustering problems
(Springer Verlag, 2017) Landa-Torres, Itziar; Manjarres, Diana; Gil-López, Sergio; Del Ser, Javier; Sanz, Sancho Salcedo; Del Ser, Javier; Tecnalia Research & Innovation; IA
The problem of partitioning a data set into disjoint groups or clusters of related items plays a key role in data analytics, in particular when the information retrieval becomes crucial for further data analysis. In this context, clustering approaches aim at obtaining a good partition of the data based on multiple criteria. One of the most challenging aspects of clustering techniques is the inference of the optimal number of clusters. In this regard, a number of clustering methods from the literature assume that the number of clusters is known a priori and subsequently assign instances to clusters based on distance, density or any other criterion. This paper proposes to override any prior assumption on the number of clusters or groups in the data at hand by hybridizing the grouping encoding strategy and the Harmony Search (HS) algorithm. The resulting hybrid approach optimally infers the number of clusters by means of the tailored design of the HS operators, which estimates this important structural clustering parameter as an implicit byproduct of the instance-to-cluster mapping performed by the algorithm. Apart from inferring the optimal number of clusters, simulation results verify that the proposed scheme achieves a better performance than other naïve clustering techniques in synthetic scenarios and widely known data repositories.
A novel grouping heuristic algorithm for the switch location problem based on a hybrid dual harmony search technique
(2011) Gil-Lopez, Sergio; Landa-Torres, Itziar; Del Ser, Javier; Salcedo-Sanz, Sancho; Manjarres, Diana; Portilla-Figueras, Jose A.; IA; Tecnalia Research & Innovation
This manuscript proposes a novel iterative approach for the so-called Switch Location Problem (SLP) based on the hybridization of a group-encoded Harmony Search combinatorial heuristic (GHS) with local search and repair methods. Our contribution over other avantgarde techniques lies on the dual application of the GHS operators over both the assignment and the grouping parts of the encoded solutions. Furthermore, the aforementioned local search and repair procedures account for the compliancy of the iteratively refined candidate solutions with respect to the capacity constraints imposed in the SLP problem. Extensive simulation results done for a wide range of network instances verify that statistically our proposed dual algorithm outperforms all existing evolutionary approaches in the literature for the specific SLP problem at hand. Furthermore, it is shown that by properly selecting different yet optimized values for the operational GHS parameters to the two parts comprising the group-encoded solutions, the algorithm can trade statistical stability (i.e. lower standard deviation of the metric) for accuracy (i.e. lower minimum value of the metric) in the set of performed simulations.
A novel heuristic approach for distance- and connectivity-based multihop node localization in wireless sensor networks
(2013-01) Manjarres, Diana; Del Ser, Javier; Gil-Lopez, Sergio; Vecchio, Massimo; Landa-Torres, Itziar; Lopez-Valcarce, Roberto; IA; Tecnalia Research & Innovation
The availability of accurate location information of constituent nodes becomes essential in many applications of wireless sensor networks. In this context, we focus on anchor-based networks where the position of some few nodes are assumed to be fixed and known a priori, whereas the location of all other nodes is to be estimated based on noisy pairwise distance measurements. This localization task embodies a non-convex optimization problem which gets even more involved by the fact that the network may not be uniquely localizable, especially when its connectivity is not sufficiently high. To efficiently tackle this problem, we present a novel soft computing approach based on a hybridization of the Harmony Search (HS) algorithm with a local search procedure that iteratively alleviates the aforementioned non-uniqueness of sparse network deployments. Furthermore, the areas in which sensor nodes can be located are limited by means of connectivity-based geometrical constraints. Extensive simulation results show that the proposed approach outperforms previously published soft computing localization techniques in most of the simulated topologies. In particular, to assess the effectiveness of the technique, we compare its performance, in terms of Normalized Localization Error (NLE), to that of Simulated Annealing (SA)-based and Particle Swarm Optimization (PSO)-based techniques, as well as a naive implementation of a Genetic Algorithm (GA) incorporating the same local search procedure here proposed. Non-parametric hypothesis tests are also used so as to shed light on the statistical significance of the obtained results.
A Novel Heuristic Approach for the Simultaneous Selection of the Optimal Clustering Method and Its Internal Parameters for Time Series Data
(Springer Verlag, 2020) Navajas-Guerrero, Adriana; Manjarres, Diana; Portillo, Eva; Landa-Torres, Itziar; Martínez Álvarez, Francisco; Troncoso Lora, Alicia; Sáez Muñoz, José António; Corchado, Emilio; Quintián, Héctor; IA
Clustering methods have become popular in the last years due to the need of analyzing the high amount of collected data from different fields of knowledge. Nevertheless, the main drawback of clustering is the selection of the optimal method along with its internal parameters in an unsupervised environment. In the present paper, a novel heuristic approach based on the Harmony Search algorithm aided with a local search procedure is presented for simultaneously optimizing the best clustering algorithm (K-means, DBSCAN and Hierarchical clustering) and its optimal internal parameters based on the Silhouette index. Extensive simulation results show that the presented approach outperforms the standard clustering configurations and also other works in the literature in different Time Series and synthetic databases.
Novel Light Coupling Systems Devised Using a Harmony Search Algorithm Approach
(SPRINGER-VERLAG BERLIN, HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY, 2017-03) Andonegui, Imanol; Landa-Torres, Itziar; Manjarres, Diana; Garcia-Adeva, Angel J.; Del Ser, Javier; Tecnalia Research & Innovation; IA
We report a critical assessment of the use of an Inverse Design (ID) approach steamed by an improved Harmony Search (IHS) algorithm for enhancing light coupling to densely integrated photonic integratic circuits (PICs) using novel grating structures. Grating couplers, performing as a very attractive vertical coupling scheme for standard silicon nano waveguides are nowadays a custom component in almost every PIC. Nevertheless, their efficiency can be highly enhanced by using our ID methodology that can deal simultaneously with many physical and geometrical parameters. Moreover, this method paves the way for designing more sophisticated non-uniform gratings, which not only match the coupling efficiency of conventional periodic corrugated waveguides, but also allow to devise more complex components such as wavelength or polarization splitters, just to cite some.
A novel multi-objective algorithm for the optimal placement of wind turbines with cost and yield production criteria
(IEEE Computer Society, 2014) Manjarres, Diana; Sanchez, Valentin; Del Ser, Javier; Landa-Torres, Itziar; Gil-Lopez, Sergio; Vande Walle, Naima; Guidon, Nicolaz; IA; HPA; Tecnalia Research & Innovation
During the last years wind energy has experimented a significant growth in comparison with other types of renewable energy sources. Accordingly, the number of wind farms has increased sharply to become one of the most developed worldwide infrastructures. Unfortunately, the high number of constraints and restrictions that must be considered nowadays when designing a wind farm deployment (e.g. protected environmental areas or geographical unfeasibility) calls for tools aimed at the cost-effective optimal placement of wind farms, along with an optimized micro-siting of their compounding wind turbines. In this paper a novel multi-objective adaptation of the Harmony Search meta-heuristic algorithm is developed and tested for efficiently solving the problem of optimally deploying wind turbines in wind farms, which is accomplished by simultaneously addressing two conflicting objectives: the yield production and the capital cost of the deployment. Experimental simulation results over a certain region of the Basque Country (northern Spain) will be presented and discussed so as to shed light on the practical applicability of the derived solver.
On the application of a hybrid harmony search algorithm to node localization in anchor-based wireless sensor networks
(2011) Manjarres, Diana; Del Ser, Javier; Gil-Lopez, Sergio; Vecchio, Massimo; Landa-Torres, Itziar; Lopez-Valcarce, Roberto; IA; Tecnalia Research & Innovation
In many applications based on Wireless Sensor Networks (WSNs) with static sensor nodes, the availability of accurate location information of the network nodes may become essential. The node localization problem is to estimate all the unknown node positions, based on noisy pairwise distance measurements of nodes within range of each other. Maximum Likelihood (ML) estimation results in a non-convex problem, which is further complicated by the fact that sufficient conditions for the solution to be unique are not easily identified, especially when dealing with sparse networks. Thereby, different node configurations can provide equally good fitness results, with only one of them corresponding to the real network geometry. This paper presents a novel soft-computing localization technique based on hybridizing a Harmony Search (HS) algorithm with a local search procedure whose aim is to identify the localizability issues and mitigate its effects during the iterative process. Moreover, certain connectivity-based geometrical constraints are exploited to further reduce the areas where each sensor node can be located. Simulation results show that our approach outperforms a previously proposed meta-heuristic localization scheme based on the Simulated Annealing (SA) algorithm, in terms of both localization error and computational cost.

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